FCM融合改进的GSA算法在医学图像分割中的研究  

Study of FCM Fusing Improved Gravitational Search Algorithm in Medical Image Segmentation

作　　者：冯飞[1] 刘培学[1] 李丽[1] 陈玉杰[1] FENG Fei ,LIU Pei- xue,LI Li, CHEN Yu- jie(Qingdao H uanghai College, Qingdao, Shandong 266427, Chin)

机构地区：[1]青岛黄海学院,山东青岛266427

出　　处：《计算机科学》2018年第B06期252-254,共3页Computer Science

基　　金：国家自然科学基金项目(61471224);山东省高等学校科技计划项目(J16LN80;J16LN94)资助

摘　　要：医学图像由于具有复杂性,在对其进行图像分割时存在很大的不确定性,为了提高模糊c均值聚类算法(FCM)在处理医学图像分割时的性能,提出一种新的混合方法进行图像分割。利用FCM算法将图像像素分成均匀的区域,融合引力搜索算法,将改进的引力搜索算法纳入模糊c均值聚类算法中,以找到最优聚类中心,使模糊c均值聚类的适应度函数值最小,从而提高分割效果。实验结果表明,相对于传统的聚类算法,所提算法在分割复杂的医学图像方面更具有效性。In order to improve the performance of the fuzzy c-means clustering algorithm in dealing with medical image segmentation,this paper presented a new hybrid method for image segmentation.The method uses fuzzy c-means clustering algorithm（FCM）to divide image pixel space into homogeneous area.Gravitational search algorithm is fused is putted into the fuzzy c-means clustering algorithm to find the optimal clustering center and make the fitness function value of fuzzy c-means clustering minimal.Experimental results show that compared with traditional clustering algorithm,this method is more effective in the segmentation of different types of images.

关 键 词：FCM 引力搜索算法 分割 聚类中心 

分 类 号：TN911.73[电子电信—通信与信息系统]